用检索提示和元数据引导生成，提升伤口护理问答的准确性和相关性。
Mined Prompting and Metadata-Guided Generation for Wound Care Visual Question Answering
- 通过检索最相似案例作为少样本示例，增强生成连贯性。
- 引入4个关键元数据属性，使回答临床精度提升。
- 适合医疗AI开发与远程护理系统优化的研究者参考。
异步远程护理的快速发展加剧了医护人员的工作负担，亟需AI系统协助高效处理患者关于伤口护理的图文咨询。2025年MEDIQA-WV共享任务聚焦于生成自由文本回答，回应配图的伤口护理问题。本文针对英文赛道提出两种互补方法：第一种采用挖掘提示策略，将训练数据嵌入向量空间，生成时检索前k个最相似示例作为少样本示范；第二种基于元数据消融研究，识别出四个能持续提升回答质量的元数据属性，训练分类器预测测试用例的属性，并根据置信度动态调整生成内容。实验表明，挖掘提示提升了回答的相关性，而元数据引导生成进一步提高了临床精确度。两项技术共同展示了构建可靠、高效的伤口护理辅助AI工具的可行方向。
原文摘要 · Abstract (English)
The rapid expansion of asynchronous remote care has intensified provider workload, creating demand for AI systems that can assist clinicians in managing patient queries more efficiently. The MEDIQA-WV 2025 shared task addresses this challenge by focusing on generating free-text responses to wound care queries paired with images. In this work, we present two complementary approaches developed for the English track. The first leverages a mined prompting strategy, where training data is embedded and the top-k most similar examples are retrieved to serve as few-shot demonstrations during generation. The second approach builds on a metadata ablation study, which identified four metadata attributes that consistently enhance response quality. We train classifiers to predict these attributes for test cases and incorporate them into the generation pipeline, dynamically adjusting outputs based on prediction confidence. Experimental results demonstrate that mined prompting improves response relevance, while metadata-guided generation further refines clinical precision. Together, these methods highlight promising directions for developing AI-driven tools that can provide reliable and efficient wound care support.
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